Overview

Dataset statistics

Number of variables16
Number of observations906292
Missing cells157616
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory110.6 MiB
Average record size in memory128.0 B

Variable types

Numeric12
Categorical4

Alerts

time has a high cardinality: 37662 distinct values High cardinality
gameId is highly correlated with teamHigh correlation
frameId is highly correlated with s and 1 other fieldsHigh correlation
s is highly correlated with disHigh correlation
a is highly correlated with sHigh correlation
dis is highly correlated with sHigh correlation
team is highly correlated with gameIdHigh correlation
nflId has 39404 (4.3%) missing values Missing
jerseyNumber has 39404 (4.3%) missing values Missing
o has 39404 (4.3%) missing values Missing
dir has 39404 (4.3%) missing values Missing
s has 60334 (6.7%) zeros Zeros
a has 56293 (6.2%) zeros Zeros
dis has 60300 (6.7%) zeros Zeros

Reproduction

Analysis started2022-11-02 15:02:04.237892
Analysis finished2022-11-02 15:03:26.588502
Duration1 minute and 22.35 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

gameId
Real number (ℝ≥0)

HIGH CORRELATION

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2021102393
Minimum2021102100
Maximum2021102500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2022-11-02T12:03:26.636746image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2021102100
5-th percentile2021102100
Q12021102402
median2021102405
Q32021102408
95-th percentile2021102500
Maximum2021102500
Range400
Interquartile range (IQR)6

Descriptive statistics

Standard deviation80.41104086
Coefficient of variation (CV)3.978573334 × 10-8
Kurtosis8.395453966
Mean2021102393
Median Absolute Deviation (MAD)3
Skewness-2.871199535
Sum1.83170893 × 1015
Variance6465.935492
MonotonicityIncreasing
2022-11-02T12:03:26.732713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
202110240090735
10.0%
202110240580178
8.8%
202110240177970
8.6%
202110240473163
8.1%
202110250071323
7.9%
202110240371277
7.9%
202110240271047
7.8%
202110240666516
 
7.3%
202110240764492
 
7.1%
202110240964354
 
7.1%
Other values (3)175237
19.3%
ValueCountFrequency (%)
202110210057431
6.3%
202110240090735
10.0%
202110240177970
8.6%
202110240271047
7.8%
202110240371277
7.9%
202110240473163
8.1%
202110240580178
8.8%
202110240666516
7.3%
202110240764492
7.1%
202110240863250
7.0%
ValueCountFrequency (%)
202110250071323
7.9%
202110241054556
6.0%
202110240964354
7.1%
202110240863250
7.0%
202110240764492
7.1%
202110240666516
7.3%
202110240580178
8.8%
202110240473163
8.1%
202110240371277
7.9%
202110240271047
7.8%

playId
Real number (ℝ≥0)

Distinct832
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2176.401355
Minimum55
Maximum4255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2022-11-02T12:03:26.857218image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum55
5-th percentile319
Q11170
median2227
Q33233
95-th percentile3904
Maximum4255
Range4200
Interquartile range (IQR)2063

Descriptive statistics

Standard deviation1169.259945
Coefficient of variation (CV)0.5372446318
Kurtosis-1.211676161
Mean2176.401355
Median Absolute Deviation (MAD)1031
Skewness-0.09884512439
Sum1972455137
Variance1367168.819
MonotonicityNot monotonic
2022-11-02T12:03:26.980612image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22934646
 
0.5%
28554025
 
0.4%
32443427
 
0.4%
3853220
 
0.4%
37443151
 
0.3%
11773059
 
0.3%
14152921
 
0.3%
39712875
 
0.3%
20352852
 
0.3%
33922852
 
0.3%
Other values (822)873264
96.4%
ValueCountFrequency (%)
55736
 
0.1%
562346
0.3%
61736
 
0.1%
621035
0.1%
64897
 
0.1%
75989
0.1%
78759
 
0.1%
85874
 
0.1%
94989
0.1%
96805
 
0.1%
ValueCountFrequency (%)
4255782
0.1%
4226966
0.1%
4222943
0.1%
4201920
0.1%
4198851
0.1%
4180966
0.1%
4177989
0.1%
4123989
0.1%
41011311
0.1%
4077713
0.1%

nflId
Real number (ℝ≥0)

MISSING

Distinct974
Distinct (%)0.1%
Missing39404
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean46021.23712
Minimum25511
Maximum54006
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2022-11-02T12:03:27.107272image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum25511
5-th percentile38557
Q142765
median46094
Q348455
95-th percentile53505
Maximum54006
Range28495
Interquartile range (IQR)5690

Descriptive statistics

Standard deviation4982.508709
Coefficient of variation (CV)0.1082654231
Kurtosis0.005408309907
Mean46021.23712
Median Absolute Deviation (MAD)3195
Skewness-0.2144367005
Sum3.989525821 × 1010
Variance24825393.04
MonotonicityNot monotonic
2022-11-02T12:03:27.227693image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
433672516
 
0.3%
391462516
 
0.3%
453302516
 
0.3%
461522516
 
0.3%
536552516
 
0.3%
534922516
 
0.3%
462212516
 
0.3%
479712516
 
0.3%
479062510
 
0.3%
412222510
 
0.3%
Other values (964)841740
92.9%
(Missing)39404
 
4.3%
ValueCountFrequency (%)
255111072
0.1%
295501639
0.2%
298511405
0.2%
30842359
 
< 0.1%
308691565
0.2%
330841539
0.2%
331071297
0.1%
33130192
 
< 0.1%
331311167
0.1%
33138643
 
0.1%
ValueCountFrequency (%)
5400675
 
< 0.1%
53959225
 
< 0.1%
539571255
0.1%
539531565
0.2%
53946357
 
< 0.1%
53933721
0.1%
53910465
 
0.1%
539001018
0.1%
53876365
 
< 0.1%
53740565
 
0.1%

frameId
Real number (ℝ≥0)

HIGH CORRELATION

Distinct175
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.2447721
Minimum1
Maximum175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2022-11-02T12:03:27.518928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median22
Q333
95-th percentile55
Maximum175
Range174
Interquartile range (IQR)22

Descriptive statistics

Standard deviation17.4079648
Coefficient of variation (CV)0.7180090094
Kurtosis6.558688704
Mean24.2447721
Median Absolute Deviation (MAD)11
Skewness1.655587672
Sum21972843
Variance303.0372386
MonotonicityNot monotonic
2022-11-02T12:03:27.645180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
121091
 
2.3%
1421091
 
2.3%
2421091
 
2.3%
2321091
 
2.3%
221091
 
2.3%
2121091
 
2.3%
2021091
 
2.3%
1921091
 
2.3%
1821091
 
2.3%
1721091
 
2.3%
Other values (165)695382
76.7%
ValueCountFrequency (%)
121091
2.3%
221091
2.3%
321091
2.3%
421091
2.3%
521091
2.3%
621091
2.3%
721091
2.3%
821091
2.3%
921091
2.3%
1021091
2.3%
ValueCountFrequency (%)
17523
< 0.1%
17423
< 0.1%
17323
< 0.1%
17223
< 0.1%
17123
< 0.1%
17023
< 0.1%
16923
< 0.1%
16823
< 0.1%
16723
< 0.1%
16623
< 0.1%

time
Categorical

HIGH CARDINALITY

Distinct37662
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
2021-10-24T17:42:21.300
 
69
2021-10-24T17:42:22.400
 
69
2021-10-24T17:42:22.600
 
69
2021-10-24T17:42:22.700
 
69
2021-10-24T17:42:22.800
 
69
Other values (37657)
905947 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters20844716
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row2021-10-22T00:23:17.300
2nd row2021-10-22T00:23:17.400
3rd row2021-10-22T00:23:17.500
4th row2021-10-22T00:23:17.600
5th row2021-10-22T00:23:17.700

Common Values

ValueCountFrequency (%)
2021-10-24T17:42:21.30069
 
< 0.1%
2021-10-24T17:42:22.40069
 
< 0.1%
2021-10-24T17:42:22.60069
 
< 0.1%
2021-10-24T17:42:22.70069
 
< 0.1%
2021-10-24T17:42:22.80069
 
< 0.1%
2021-10-24T17:42:22.90069
 
< 0.1%
2021-10-24T17:42:23.00069
 
< 0.1%
2021-10-24T17:42:23.10069
 
< 0.1%
2021-10-24T17:42:23.20069
 
< 0.1%
2021-10-24T17:42:23.30069
 
< 0.1%
Other values (37652)905602
99.9%

Length

2022-11-02T12:03:27.768749image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-10-24t17:42:21.30069
 
< 0.1%
2021-10-24t17:42:21.00069
 
< 0.1%
2021-10-24t20:07:02.10069
 
< 0.1%
2021-10-24t19:27:43.80069
 
< 0.1%
2021-10-24t17:42:22.50069
 
< 0.1%
2021-10-24t18:20:59.30069
 
< 0.1%
2021-10-24t17:42:22.30069
 
< 0.1%
2021-10-24t18:20:59.00069
 
< 0.1%
2021-10-24t17:42:22.20069
 
< 0.1%
2021-10-24t18:20:58.90069
 
< 0.1%
Other values (37652)905602
99.9%

Most occurring characters

ValueCountFrequency (%)
04496869
21.6%
23803532
18.2%
12958652
14.2%
-1812584
8.7%
:1812584
8.7%
41305503
 
6.3%
T906292
 
4.3%
.906292
 
4.3%
5622587
 
3.0%
3601658
 
2.9%
Other values (4)1618163
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15406964
73.9%
Other Punctuation2718876
 
13.0%
Dash Punctuation1812584
 
8.7%
Uppercase Letter906292
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04496869
29.2%
23803532
24.7%
12958652
19.2%
41305503
 
8.5%
5622587
 
4.0%
3601658
 
3.9%
9453997
 
2.9%
7413147
 
2.7%
8395255
 
2.6%
6355764
 
2.3%
Other Punctuation
ValueCountFrequency (%)
:1812584
66.7%
.906292
33.3%
Dash Punctuation
ValueCountFrequency (%)
-1812584
100.0%
Uppercase Letter
ValueCountFrequency (%)
T906292
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common19938424
95.7%
Latin906292
 
4.3%

Most frequent character per script

Common
ValueCountFrequency (%)
04496869
22.6%
23803532
19.1%
12958652
14.8%
-1812584
9.1%
:1812584
9.1%
41305503
 
6.5%
.906292
 
4.5%
5622587
 
3.1%
3601658
 
3.0%
9453997
 
2.3%
Other values (3)1164166
 
5.8%
Latin
ValueCountFrequency (%)
T906292
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII20844716
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04496869
21.6%
23803532
18.2%
12958652
14.2%
-1812584
8.7%
:1812584
8.7%
41305503
 
6.3%
T906292
 
4.3%
.906292
 
4.3%
5622587
 
3.0%
3601658
 
2.9%
Other values (4)1618163
 
7.8%

jerseyNumber
Real number (ℝ≥0)

MISSING

Distinct99
Distinct (%)< 0.1%
Missing39404
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean49.35768173
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2022-11-02T12:03:27.887108image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q122
median51
Q375
95-th percentile95
Maximum99
Range98
Interquartile range (IQR)53

Descriptive statistics

Standard deviation29.80968417
Coefficient of variation (CV)0.603952275
Kurtosis-1.321476716
Mean49.35768173
Median Absolute Deviation (MAD)27
Skewness0.03295354894
Sum42787582
Variance888.6172703
MonotonicityNot monotonic
2022-11-02T12:03:28.038454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2119172
 
2.1%
2417369
 
1.9%
217149
 
1.9%
2215586
 
1.7%
5115008
 
1.7%
2314680
 
1.6%
7214554
 
1.6%
114406
 
1.6%
2913786
 
1.5%
2513630
 
1.5%
Other values (89)711548
78.5%
(Missing)39404
 
4.3%
ValueCountFrequency (%)
114406
1.6%
217149
1.9%
32871
 
0.3%
410684
1.2%
58293
0.9%
66249
 
0.7%
73149
 
0.3%
89373
1.0%
97514
0.8%
1013284
1.5%
ValueCountFrequency (%)
9911337
1.3%
9811084
1.2%
9712753
1.4%
968001
0.9%
957228
0.8%
9411312
1.2%
937789
0.9%
928563
0.9%
9113350
1.5%
9012874
1.4%

team
Categorical

HIGH CORRELATION

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
BAL
 
43395
CIN
 
43395
football
 
39404
TEN
 
38346
KC
 
38346
Other values (22)
703406 

Length

Max length8
Median length3
Mean length2.926798427
Min length2

Characters and Unicode

Total characters2652534
Distinct characters29
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDEN
2nd rowDEN
3rd rowDEN
4th rowDEN
5th rowDEN

Common Values

ValueCountFrequency (%)
BAL43395
 
4.8%
CIN43395
 
4.8%
football39404
 
4.3%
TEN38346
 
4.2%
KC38346
 
4.2%
GB37290
 
4.1%
WAS37290
 
4.1%
NYG34991
 
3.9%
CAR34991
 
3.9%
SEA34111
 
3.8%
Other values (17)524733
57.9%

Length

2022-11-02T12:03:28.155905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bal43395
 
4.8%
cin43395
 
4.8%
football39404
 
4.3%
ten38346
 
4.2%
kc38346
 
4.2%
gb37290
 
4.1%
was37290
 
4.1%
nyg34991
 
3.9%
car34991
 
3.9%
sea34111
 
3.8%
Other values (17)524733
57.9%

Most occurring characters

ValueCountFrequency (%)
A279807
 
10.5%
N272580
 
10.3%
I195338
 
7.4%
E193292
 
7.3%
C174977
 
6.6%
L167497
 
6.3%
T134915
 
5.1%
B111463
 
4.2%
S97493
 
3.7%
H91872
 
3.5%
Other values (19)933300
35.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2337302
88.1%
Lowercase Letter315232
 
11.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A279807
12.0%
N272580
11.7%
I195338
 
8.4%
E193292
 
8.3%
C174977
 
7.5%
L167497
 
7.2%
T134915
 
5.8%
B111463
 
4.8%
S97493
 
4.2%
H91872
 
3.9%
Other values (13)618068
26.4%
Lowercase Letter
ValueCountFrequency (%)
o78808
25.0%
l78808
25.0%
b39404
12.5%
t39404
12.5%
f39404
12.5%
a39404
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin2652534
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A279807
 
10.5%
N272580
 
10.3%
I195338
 
7.4%
E193292
 
7.3%
C174977
 
6.6%
L167497
 
6.3%
T134915
 
5.1%
B111463
 
4.2%
S97493
 
3.7%
H91872
 
3.5%
Other values (19)933300
35.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2652534
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A279807
 
10.5%
N272580
 
10.3%
I195338
 
7.4%
E193292
 
7.3%
C174977
 
6.6%
L167497
 
6.3%
T134915
 
5.1%
B111463
 
4.2%
S97493
 
3.7%
H91872
 
3.5%
Other values (19)933300
35.2%

playDirection
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
left
468211 
right
438081 

Length

Max length5
Median length4
Mean length4.483377322
Min length4

Characters and Unicode

Total characters4063249
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowleft
2nd rowleft
3rd rowleft
4th rowleft
5th rowleft

Common Values

ValueCountFrequency (%)
left468211
51.7%
right438081
48.3%

Length

2022-11-02T12:03:28.248839image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-02T12:03:28.339225image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
left468211
51.7%
right438081
48.3%

Most occurring characters

ValueCountFrequency (%)
t906292
22.3%
l468211
11.5%
e468211
11.5%
f468211
11.5%
r438081
10.8%
i438081
10.8%
g438081
10.8%
h438081
10.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4063249
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t906292
22.3%
l468211
11.5%
e468211
11.5%
f468211
11.5%
r438081
10.8%
i438081
10.8%
g438081
10.8%
h438081
10.8%

Most occurring scripts

ValueCountFrequency (%)
Latin4063249
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t906292
22.3%
l468211
11.5%
e468211
11.5%
f468211
11.5%
r438081
10.8%
i438081
10.8%
g438081
10.8%
h438081
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII4063249
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t906292
22.3%
l468211
11.5%
e468211
11.5%
f468211
11.5%
r438081
10.8%
i438081
10.8%
g438081
10.8%
h438081
10.8%

x
Real number (ℝ≥0)

Distinct11707
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.73619214
Minimum0.58
Maximum120.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2022-11-02T12:03:28.432774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.58
5-th percentile21.29
Q140.96
median60.8
Q380.36
95-th percentile100.32
Maximum120.94
Range120.36
Interquartile range (IQR)39.4

Descriptive statistics

Standard deviation24.59857675
Coefficient of variation (CV)0.4050068976
Kurtosis-0.8639535791
Mean60.73619214
Median Absolute Deviation (MAD)19.69
Skewness-0.009987463377
Sum55044725.05
Variance605.0899781
MonotonicityNot monotonic
2022-11-02T12:03:28.563289image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46.99186
 
< 0.1%
65.9174
 
< 0.1%
63.24172
 
< 0.1%
85.36172
 
< 0.1%
82.15169
 
< 0.1%
47.85168
 
< 0.1%
33.33167
 
< 0.1%
59.7167
 
< 0.1%
47.7167
 
< 0.1%
48.34166
 
< 0.1%
Other values (11697)904584
99.8%
ValueCountFrequency (%)
0.581
< 0.1%
0.591
< 0.1%
0.631
< 0.1%
0.711
< 0.1%
0.731
< 0.1%
0.771
< 0.1%
0.782
< 0.1%
0.82
< 0.1%
0.831
< 0.1%
0.842
< 0.1%
ValueCountFrequency (%)
120.941
< 0.1%
120.471
< 0.1%
120.011
< 0.1%
119.811
< 0.1%
119.551
< 0.1%
119.491
< 0.1%
119.392
< 0.1%
119.291
< 0.1%
119.241
< 0.1%
119.21
< 0.1%

y
Real number (ℝ)

Distinct5422
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.81395279
Minimum-1.67
Maximum57.72
Zeros3
Zeros (%)< 0.1%
Negative53
Negative (%)< 0.1%
Memory size6.9 MiB
2022-11-02T12:03:28.696726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1.67
5-th percentile11.6
Q122.04
median26.8
Q331.59
95-th percentile41.86
Maximum57.72
Range59.39
Interquartile range (IQR)9.55

Descriptive statistics

Standard deviation8.354167749
Coefficient of variation (CV)0.3115604706
Kurtosis0.3241012869
Mean26.81395279
Median Absolute Deviation (MAD)4.78
Skewness-0.009097800801
Sum24301270.9
Variance69.79211877
MonotonicityNot monotonic
2022-11-02T12:03:28.820986image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.81981
 
0.1%
29.82952
 
0.1%
29.83948
 
0.1%
23.81942
 
0.1%
23.87927
 
0.1%
29.74917
 
0.1%
23.83909
 
0.1%
29.75909
 
0.1%
23.78908
 
0.1%
23.85907
 
0.1%
Other values (5412)896992
99.0%
ValueCountFrequency (%)
-1.671
< 0.1%
-1.311
< 0.1%
-0.971
< 0.1%
-0.931
< 0.1%
-0.912
< 0.1%
-0.871
< 0.1%
-0.861
< 0.1%
-0.791
< 0.1%
-0.781
< 0.1%
-0.691
< 0.1%
ValueCountFrequency (%)
57.721
< 0.1%
56.891
< 0.1%
56.071
< 0.1%
55.81
< 0.1%
55.591
< 0.1%
55.411
< 0.1%
55.311
< 0.1%
55.281
< 0.1%
55.271
< 0.1%
55.051
< 0.1%

s
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2091
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.596570035
Minimum0
Maximum27.83
Zeros60334
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2022-11-02T12:03:28.952604image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median2.16
Q33.85
95-th percentile6.79
Maximum27.83
Range27.83
Interquartile range (IQR)3.1

Descriptive statistics

Standard deviation2.391158449
Coefficient of variation (CV)0.920891182
Kurtosis13.63636117
Mean2.596570035
Median Absolute Deviation (MAD)1.52
Skewness2.275058694
Sum2353250.65
Variance5.717638727
MonotonicityNot monotonic
2022-11-02T12:03:29.073835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
060334
 
6.7%
0.0114915
 
1.6%
0.028560
 
0.9%
0.036057
 
0.7%
0.044944
 
0.5%
0.054146
 
0.5%
0.063720
 
0.4%
0.073418
 
0.4%
0.083184
 
0.4%
0.093048
 
0.3%
Other values (2081)793966
87.6%
ValueCountFrequency (%)
060334
6.7%
0.0114915
 
1.6%
0.028560
 
0.9%
0.036057
 
0.7%
0.044944
 
0.5%
0.054146
 
0.5%
0.063720
 
0.4%
0.073418
 
0.4%
0.083184
 
0.4%
0.093048
 
0.3%
ValueCountFrequency (%)
27.831
< 0.1%
27.671
< 0.1%
27.521
< 0.1%
27.461
< 0.1%
27.421
< 0.1%
27.381
< 0.1%
27.222
< 0.1%
27.211
< 0.1%
27.051
< 0.1%
26.991
< 0.1%

a
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1497
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.798458025
Minimum0
Maximum30.03
Zeros56293
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2022-11-02T12:03:29.205571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.71
median1.54
Q32.6
95-th percentile4.49
Maximum30.03
Range30.03
Interquartile range (IQR)1.89

Descriptive statistics

Standard deviation1.447153974
Coefficient of variation (CV)0.8046637479
Kurtosis5.28825985
Mean1.798458025
Median Absolute Deviation (MAD)0.92
Skewness1.350708611
Sum1629928.12
Variance2.094254626
MonotonicityNot monotonic
2022-11-02T12:03:29.330810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
056293
 
6.2%
0.0111832
 
1.3%
0.026598
 
0.7%
0.035140
 
0.6%
0.044086
 
0.5%
0.053443
 
0.4%
0.062936
 
0.3%
0.962864
 
0.3%
1.322839
 
0.3%
12837
 
0.3%
Other values (1487)807424
89.1%
ValueCountFrequency (%)
056293
6.2%
0.0111832
 
1.3%
0.026598
 
0.7%
0.035140
 
0.6%
0.044086
 
0.5%
0.053443
 
0.4%
0.062936
 
0.3%
0.072611
 
0.3%
0.082470
 
0.3%
0.092157
 
0.2%
ValueCountFrequency (%)
30.031
< 0.1%
26.131
< 0.1%
24.681
< 0.1%
24.611
< 0.1%
23.261
< 0.1%
22.931
< 0.1%
22.51
< 0.1%
21.551
< 0.1%
21.321
< 0.1%
21.291
< 0.1%

dis
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct517
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2631815022
Minimum0
Maximum6.42
Zeros60300
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2022-11-02T12:03:29.464958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.08
median0.22
Q30.39
95-th percentile0.68
Maximum6.42
Range6.42
Interquartile range (IQR)0.31

Descriptive statistics

Standard deviation0.2550544709
Coefficient of variation (CV)0.9691200511
Kurtosis46.29641698
Mean0.2631815022
Median Absolute Deviation (MAD)0.15
Skewness4.017486438
Sum238519.29
Variance0.0650527831
MonotonicityNot monotonic
2022-11-02T12:03:29.587528image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
060300
 
6.7%
0.0148320
 
5.3%
0.0226988
 
3.0%
0.0321073
 
2.3%
0.0418584
 
2.1%
0.0517004
 
1.9%
0.1916666
 
1.8%
0.1716557
 
1.8%
0.1816415
 
1.8%
0.1616409
 
1.8%
Other values (507)647976
71.5%
ValueCountFrequency (%)
060300
6.7%
0.0148320
5.3%
0.0226988
3.0%
0.0321073
 
2.3%
0.0418584
 
2.1%
0.0517004
 
1.9%
0.0616101
 
1.8%
0.0715838
 
1.7%
0.0815513
 
1.7%
0.0915559
 
1.7%
ValueCountFrequency (%)
6.421
< 0.1%
6.42
< 0.1%
6.381
< 0.1%
6.161
< 0.1%
6.121
< 0.1%
6.031
< 0.1%
6.011
< 0.1%
5.891
< 0.1%
5.881
< 0.1%
5.851
< 0.1%

o
Real number (ℝ≥0)

MISSING

Distinct36001
Distinct (%)4.2%
Missing39404
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean178.3109756
Minimum0
Maximum360
Zeros7
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2022-11-02T12:03:29.719072image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28.51
Q187.23
median177.66
Q3267.98
95-th percentile332
Maximum360
Range360
Interquartile range (IQR)180.75

Descriptive statistics

Standard deviation100.0687965
Coefficient of variation (CV)0.561203797
Kurtosis-1.348592003
Mean178.3109756
Median Absolute Deviation (MAD)90.37
Skewness0.01879387761
Sum154575645
Variance10013.76404
MonotonicityNot monotonic
2022-11-02T12:03:29.844496image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
901259
 
0.1%
267.8798
 
< 0.1%
271.1893
 
< 0.1%
266.9786
 
< 0.1%
260.0485
 
< 0.1%
82.1584
 
< 0.1%
269.2883
 
< 0.1%
263.0282
 
< 0.1%
97.8782
 
< 0.1%
96.1781
 
< 0.1%
Other values (35991)864855
95.4%
(Missing)39404
 
4.3%
ValueCountFrequency (%)
07
 
< 0.1%
0.0125
< 0.1%
0.0214
< 0.1%
0.0320
< 0.1%
0.0415
< 0.1%
0.0520
< 0.1%
0.0616
< 0.1%
0.0717
< 0.1%
0.0819
< 0.1%
0.099
 
< 0.1%
ValueCountFrequency (%)
36012
< 0.1%
359.9912
< 0.1%
359.9813
< 0.1%
359.9714
< 0.1%
359.9615
< 0.1%
359.9520
< 0.1%
359.949
< 0.1%
359.9311
< 0.1%
359.929
< 0.1%
359.9117
< 0.1%

dir
Real number (ℝ≥0)

MISSING

Distinct36001
Distinct (%)4.2%
Missing39404
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean181.2278835
Minimum0
Maximum360
Zeros13
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2022-11-02T12:03:29.981052image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile23.7
Q191.13
median180.76
Q3271.31
95-th percentile337.62
Maximum360
Range360
Interquartile range (IQR)180.18

Descriptive statistics

Standard deviation101.3668017
Coefficient of variation (CV)0.559333364
Kurtosis-1.283033517
Mean181.2278835
Median Absolute Deviation (MAD)90.09
Skewness-0.005686777698
Sum157104277.4
Variance10275.22849
MonotonicityNot monotonic
2022-11-02T12:03:30.266697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90.3663
 
< 0.1%
269.1563
 
< 0.1%
270.9562
 
< 0.1%
93.9761
 
< 0.1%
270.7561
 
< 0.1%
266.6860
 
< 0.1%
97.7660
 
< 0.1%
88.0160
 
< 0.1%
267.2760
 
< 0.1%
90.3759
 
< 0.1%
Other values (35991)866279
95.6%
(Missing)39404
 
4.3%
ValueCountFrequency (%)
013
< 0.1%
0.0122
< 0.1%
0.0225
< 0.1%
0.0322
< 0.1%
0.0421
< 0.1%
0.0524
< 0.1%
0.0623
< 0.1%
0.0726
< 0.1%
0.0818
< 0.1%
0.0912
< 0.1%
ValueCountFrequency (%)
3605
 
< 0.1%
359.9928
< 0.1%
359.9816
< 0.1%
359.9723
< 0.1%
359.969
 
< 0.1%
359.9517
< 0.1%
359.9418
< 0.1%
359.9318
< 0.1%
359.9230
< 0.1%
359.9117
< 0.1%

event
Categorical

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
None
838465 
ball_snap
 
20999
pass_forward
 
18469
autoevent_ballsnap
 
8947
autoevent_passforward
 
8602
Other values (17)
 
10810

Length

Max length25
Median length4
Mean length4.653080905
Min length3

Characters and Unicode

Total characters4217050
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None838465
92.5%
ball_snap20999
 
2.3%
pass_forward18469
 
2.0%
autoevent_ballsnap8947
 
1.0%
autoevent_passforward8602
 
0.9%
play_action5060
 
0.6%
run1265
 
0.1%
qb_sack1150
 
0.1%
pass_arrived851
 
0.1%
line_set552
 
0.1%
Other values (12)1932
 
0.2%

Length

2022-11-02T12:03:30.394968image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none838465
92.5%
ball_snap20999
 
2.3%
pass_forward18469
 
2.0%
autoevent_ballsnap8947
 
1.0%
autoevent_passforward8602
 
0.9%
play_action5060
 
0.6%
run1265
 
0.1%
qb_sack1150
 
0.1%
pass_arrived851
 
0.1%
line_set552
 
0.1%
Other values (12)1932
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n895574
21.2%
o890100
21.1%
e877818
20.8%
N838465
19.9%
a146740
 
3.5%
s89700
 
2.1%
_67114
 
1.6%
l65596
 
1.6%
p64722
 
1.5%
r58443
 
1.4%
Other values (15)222778
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3311471
78.5%
Uppercase Letter838465
 
19.9%
Connector Punctuation67114
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n895574
27.0%
o890100
26.9%
e877818
26.5%
a146740
 
4.4%
s89700
 
2.7%
l65596
 
2.0%
p64722
 
2.0%
r58443
 
1.8%
t44252
 
1.3%
b31395
 
0.9%
Other values (13)147131
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
N838465
100.0%
Connector Punctuation
ValueCountFrequency (%)
_67114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4149936
98.4%
Common67114
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
n895574
21.6%
o890100
21.4%
e877818
21.2%
N838465
20.2%
a146740
 
3.5%
s89700
 
2.2%
l65596
 
1.6%
p64722
 
1.6%
r58443
 
1.4%
t44252
 
1.1%
Other values (14)178526
 
4.3%
Common
ValueCountFrequency (%)
_67114
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4217050
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n895574
21.2%
o890100
21.1%
e877818
20.8%
N838465
19.9%
a146740
 
3.5%
s89700
 
2.1%
_67114
 
1.6%
l65596
 
1.6%
p64722
 
1.5%
r58443
 
1.4%
Other values (15)222778
 
5.3%

Interactions

2022-11-02T12:03:19.251473image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:45.739950image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:48.864478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:51.727444image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:54.866873image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:57.865867image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:00.833260image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:03.922162image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:06.886846image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:09.867653image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:13.058222image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:16.020888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:19.513452image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:45.996269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:49.106752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:51.980932image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:55.117244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:58.117921image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:01.090438image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:04.170110image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:07.138253image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:10.123088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:13.306784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:16.284995image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:19.763080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:46.368965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:49.337510image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:52.220631image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:55.357270image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:58.359424image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-02T12:03:10.527511image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:13.548360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:16.532233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:20.017418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:46.615117image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:49.577752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:52.467724image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:55.600683image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:58.604723image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:01.579337image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:04.658376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-02T12:03:10.779285image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:13.795237image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:16.791987image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:20.272657image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:46.873392image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:49.818514image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:52.714085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:55.850754image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:58.845576image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:01.827333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:04.912101image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:07.898513image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:11.035064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:14.043775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:17.049070image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:20.522132image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:47.120533image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:50.055501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:52.958563image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:56.108174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:59.086679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:02.067557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:05.150571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:08.139943image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:11.292369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:14.286546image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:17.307736image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:20.774393image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:47.375514image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:50.292969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:53.204083image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:56.373278image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:59.334370image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:02.465367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:05.388129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:08.386674image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:11.552296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:14.529788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:17.567394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:21.024892image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:47.620425image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:50.530087image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:53.454213image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:56.624771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:59.575070image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:02.707051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:05.632164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:08.631266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:11.805574image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:14.773571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:17.821054image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:21.274596image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:47.868435image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:50.766339image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:53.708091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:56.866747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:59.817333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:02.945262image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:05.870852image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:08.873980image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:12.048880image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:15.014418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:18.072885image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:21.533526image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:48.115293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:51.001988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:53.967443image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:57.116051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:00.078843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:03.185929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:06.112102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:09.117626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:12.295441image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:15.255179image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:18.488100image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:21.791459image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:48.363158image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:51.241951image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:54.375101image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:57.364315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:00.334345image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:03.430619image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:06.360485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:09.368685image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:12.551410image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:15.503186image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:18.739889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:22.046107image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:48.617121image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:51.485865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:54.623780image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:02:57.616590image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:00.584808image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:03.681500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:06.605484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:09.621392image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:12.811905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:15.756605image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:03:18.998146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-02T12:03:30.497572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-02T12:03:30.650225image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-02T12:03:30.796767image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-02T12:03:30.944292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-02T12:03:31.082385image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-02T12:03:31.191782image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-02T12:03:22.558017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-02T12:03:23.671202image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-02T12:03:25.416901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-11-02T12:03:26.004930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

gameIdplayIdnflIdframeIdtimejerseyNumberteamplayDirectionxysadisodirevent
020211021005635459.012021-10-22T00:23:17.30022.0DENleft75.4220.770.971.030.0960.7564.69None
120211021005635459.022021-10-22T00:23:17.40022.0DENleft75.5120.821.090.970.1163.0660.90None
220211021005635459.032021-10-22T00:23:17.50022.0DENleft75.6120.881.230.790.1263.0661.13None
320211021005635459.042021-10-22T00:23:17.60022.0DENleft75.7320.941.300.570.1362.3560.85None
420211021005635459.052021-10-22T00:23:17.70022.0DENleft75.8421.011.330.370.1360.6560.99None
520211021005635459.062021-10-22T00:23:17.80022.0DENleft75.9621.071.360.220.1358.7661.66ball_snap
620211021005635459.072021-10-22T00:23:17.90022.0DENleft76.0721.141.350.050.1357.8060.75None
720211021005635459.082021-10-22T00:23:18.00022.0DENleft76.1921.211.300.340.1357.8058.86None
820211021005635459.092021-10-22T00:23:18.10022.0DENleft76.2921.281.290.480.1359.1755.39None
920211021005635459.0102021-10-22T00:23:18.20022.0DENleft76.4021.361.270.580.1360.2152.44None

Last rows

gameIdplayIdnflIdframeIdtimejerseyNumberteamplayDirectionxysadisodirevent
90628220211025003998NaN662021-10-26T03:17:05.400NaNfootballleft114.5113.753.191.190.32NaNNaNNone
90628320211025003998NaN672021-10-26T03:17:05.500NaNfootballleft114.5313.433.141.100.32NaNNaNNone
90628420211025003998NaN682021-10-26T03:17:05.600NaNfootballleft114.5613.123.061.110.31NaNNaNNone
90628520211025003998NaN692021-10-26T03:17:05.700NaNfootballleft114.5912.822.971.160.30NaNNaNNone
90628620211025003998NaN702021-10-26T03:17:05.800NaNfootballleft114.6312.532.861.200.29NaNNaNautoevent_passforward
90628720211025003998NaN712021-10-26T03:17:05.900NaNfootballleft112.9314.5222.040.442.62NaNNaNpass_forward
90628820211025003998NaN722021-10-26T03:17:06.000NaNfootballleft111.3416.0721.951.402.22NaNNaNNone
90628920211025003998NaN732021-10-26T03:17:06.100NaNfootballleft109.7417.6021.812.022.21NaNNaNNone
90629020211025003998NaN742021-10-26T03:17:06.200NaNfootballleft108.1719.1221.662.392.19NaNNaNNone
90629120211025003998NaN752021-10-26T03:17:06.300NaNfootballleft106.6220.6321.532.682.17NaNNaNNone